Benchmarking Native In-Database TPCx-AI at 100 Terabytes in Ocient Hyperscale Data Warehouse: An Eight-Use-Case Study of Classical and Statistical ML on Relational Primitives
Summary: Reports the first 100-TB TPCx-AI execution: eight classical/statistical ML pipelines run natively in SQL with relational primitives, matrices, and JIT-compiled trees. Ocient scales linearly/sub-linearly and compares favorably with algorithm-matched Spark, avoiding data extraction. (summarized by gpt-5.6-luna on Aug 28 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Jason Arnold (Ocient)
- 2. Neesh Dahiya (Ocient)
- 3. Knut Stolze (Ocient)
BibTeX Citation
@article{arnold_vldb26,
title = {{Benchmarking Native In-Database TPCx-AI at 100 Terabytes in Ocient Hyperscale Data Warehouse: An Eight-Use-Case Study of Classical and Statistical ML on Relational Primitives}},
author = {Arnold, Jason and Dahiya, Neesh and Stolze, Knut},
journal = {PVLDB},
series = {{VLDB} '26},
volume = {19},
number = {12},
pages = {4143--4155},
doi = {10.14778/3827998.3828022},
url = {https://doi.org/10.14778/3827998.3828022},
year = {2026}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 105 | The MADlib Analytics Library or MAD Skills, the SQL | 2012 | VLDB | 0.00033638251 |
| 154 | MAD Skills: New Analysis Practices for Big Data | 2009 | VLDB | 0.00028579704 |
| 179 | The Vertica Analytic Database: C-Store 7 Years Later | 2012 | VLDB | 0.00026611886 |
| 503 | Towards a Unified Architecture for in-RDBMS Analytics | 2012 | SIGMOD | 0.00017202276 |
| 1,668 | SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle | 2020 | CIDR | 9.9371612e-05 |
| 4,741 | Machine Learning for Databases | 2021 | VLDB | 6.4410027e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,601 | TQEx: Tensor-based Query Engine Enhanced by Bridging the Gap | 2026 | SIGMOD |
| 2 | 10,020 | Share the Tensor Tea: How Databases can Leverage the Machine Learning Ecosystem | 2022 | VLDB |
| 3 | 5,773 | Optimizing Data Pipelines for Machine Learning in Feature Stores | 2023 | VLDB |
| 4 | 8,274 | Scaling GPU-Accelerated Databases beyond GPU Memory Size | 2025 | VLDB |
| 5 | 1,839 | Why TPC Is Not Enough: An Analysis of the Amazon Redshift Fleet | 2024 | VLDB |
| 6 | 10,715 | TPCx-AI under the Microscope: A Benchmarking Debt Analysis | 2026 | VLDB |
| 7 | 5,991 | Terabyte-Scale Analytics in the Blink of an Eye | 2026 | VLDB |
| 8 | 10,922 | Virtualizing Recursion: Just-In-Time Graph Analytics in a Hyperscale Relational Warehouse | 2026 | VLDB |
| 9 | 2,460 | Query Processing on Tensor Computation Runtimes | 2022 | VLDB |
| 10 | 4,925 | TPCx-AI - An Industry Standard Benchmark for Artificial Intelligence and Machine Learning Systems | 2023 | VLDB |